Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2019.01.069
Title: Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior
Authors: Kashyap, Rajan 
Kong, Ru
Bhattacharjee, Sagarika
Li, Jingwei 
Zhou, Juan 
Yeo, BT Thomas 
Keywords: Science & Technology
Life Sciences & Biomedicine
Neurosciences
Neuroimaging
Radiology, Nuclear Medicine & Medical Imaging
Neurosciences & Neurology
Functional connectivity fingerprint
Elastic net
Cross-validation
Trait
State
RESTING-STATE FMRI
BRAIN CONNECTIVITY
CINGULATE CORTEX
DEFAULT MODE
NETWORK
REGULARIZATION
ORGANIZATION
WAKEFULNESS
RELIABILITY
FRAMEWORK
Issue Date: 1-Apr-2019
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation: Kashyap, Rajan, Kong, Ru, Bhattacharjee, Sagarika, Li, Jingwei, Zhou, Juan, Yeo, BT Thomas (2019-04-01). Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior. NEUROIMAGE 189 : 804-812. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2019.01.069
Abstract: © 2019 Elsevier Inc. There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.
Source Title: NEUROIMAGE
URI: https://scholarbank.nus.edu.sg/handle/10635/167720
ISSN: 10538119
10959572
DOI: 10.1016/j.neuroimage.2019.01.069
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
RAJAN_Main_181218_final.docx3.05 MBMicrosoft Word XML

OPEN

Post-printView/Download

SCOPUSTM   
Citations

17
checked on Apr 18, 2021

Page view(s)

97
checked on Apr 16, 2021

Download(s)

3
checked on Apr 16, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.